AIMC Topic: Glioblastoma

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Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning.

Radiation oncology (London, England)
BACKGROUND: Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resectio...

Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network.

Scientific reports
In managing a patient with glioblastoma (GBM), a surgeon must carefully consider whether sufficient tumour can be removed so that the patient can enjoy the benefits of decompression and cytoreduction, without impacting on the patient's neurological s...

Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

Cancer
BACKGROUND: Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represen...

A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma.

European radiology
OBJECTIVES: To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).

DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet.

Medical physics
PURPOSE: Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow-up magn...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

IEEE transactions on medical imaging
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict ove...

Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

Medical hypotheses
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems...

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Medical hypotheses
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MR...

Integration of Cancer Genomics Data for Tree-based Dimensionality Reduction and Cancer Outcome Prediction.

Molecular informatics
Accurate outcome prediction is crucial for precision medicine and personalized treatment of cancer. Researchers have found that multi-dimensional cancer omics studies outperform each data type (mRNA, microRNA, methylation or somatic copy number alter...